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Tropical forests grab carbon Tropical forests store and process large amounts of carbon, affecting the amount of CO 2 in the atmosphere, and hence the rate and magnitude of climate change. The extent of the contribution of tropical forests in this role is uncertain, largely because of a lack of monitoring. An international collaboration has now collected and analysed data from a ten-country network of 79 long-term monitoring plots across the largest tropical continent - Africa. Their findings reveal that above-ground carbon storage in live trees increased by 0.63 tonnes of carbon per hectare per year between 1968 and 2007. Extrapolation to unmeasured forest components and scaling to the continent implies a total increase in carbon storage in African tropical forest trees of 340 million tonnes a year. These results provide evidence that increasing carbon storage in old-growth forests is a pan-tropical phenomenon. This study reports data from a network of long-term monitoring plots across African tropical forests, which finds that above-ground carbon storage in live trees increased by 0.63 Mg C ha −1 yr −1 between 1968 and 2007. The data is extrapolated to unmeasured forest components, and by scaling to the continent, a total increase in carbon storage in African tropical forest trees of 0.34 Pg C yr −1 is estimated. These results provide evidence that increasing carbon storage in old-growth forests is a pan-tropical phenomenon. The response of terrestrial vegetation to a globally changing environment is central to predictions of future levels of atmospheric carbon dioxide 1 , 2 . The role of tropical forests is critical because they are carbon-dense and highly productive 3 , 4 . Inventory plots across Amazonia show that old-growth forests have increased in carbon storage over recent decades 5 , 6 , 7 , but the response of one-third of the world’s tropical forests in Africa 8 is largely unknown owing to an absence of spatially extensive observation networks 9 , 10 . Here we report data from a ten-country network of long-term monitoring plots in African tropical forests. We find that across 79 plots (163 ha) above-ground carbon storage in live trees increased by 0.63 Mg C ha -1  yr -1 between 1968 and 2007 (95% confidence interval (CI), 0.22–0.94; mean interval, 1987–96). Extrapolation to unmeasured forest components (live roots, small trees, necromass) and scaling to the continent implies a total increase in carbon storage in African tropical forest trees of 0.34 Pg C yr -1 (CI, 0.15–0.43). These reported changes in carbon storage are similar to those reported for Amazonian forests per unit area 6 , 7 , providing evidence that increasing carbon storage in old-growth forests is a pan-tropical phenomenon. Indeed, combining all standardized inventory data from this study and from tropical America and Asia 5 , 6 , 11 together yields a comparable figure of 0.49 Mg C ha -1  yr -1 ( n = 156; 562 ha; CI, 0.29–0.66; mean interval, 1987–97). This indicates a carbon sink of 1.3 Pg C yr -1 (CI, 0.8–1.6) across all tropical forests during recent decades. Taxon-specific analyses of African inventory and other data 12 suggest that widespread changes in resource availability, such as increasing atmospheric carbon dioxide concentrations, may be the cause of the increase in carbon stocks 13 , as some theory 14 and models 2 , 10 , 15 predict.

Aim: Our aim was to evaluate the extent to which we can predict and map tree alpha diversity across broad spatial scales either by using climate and remote sensing data or by exploiting spatial autocorrelation patterns. Location: Tropical rain forest, West Africa and Atlantic Central Africa. Methods: Alpha diversity estimates were compiled for trees with diameter at breast height ≥ 10 cm in 573 inventory plots. Linear regression (ordinary least squares, OLS) and random forest (RF) statistical techniques were used to project alpha diversity estimates at unsampled locations using climate data and remote sensing data [Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), Quick Scatterometer (QSCAT), tree cover, elevation]. The prediction reliabilities of OLS and RF models were evaluated using a novel approach and compared to that of a kriging model based on geographic location alone. Results: The predictive power of the kriging model was comparable to that of OLS and RF models based on climatic and remote sensing data. The three models provided congruent predictions of alpha diversity in well-sampled areas but not in poorly inventoried locations. The reliability of the predictions of all three models declined markedly with distance from points with inventory data, becoming very low at distances > 50 km. According to inventory data, Atlantic Central African forests display a higher mean alpha diversity than do West African forests. Main conclusions: The lower tree alpha diversity in West Africa than in Atlantic Central Africa may reflect a richer regional species pool in the latter. Our results emphasize and illustrate the need to test model predictions in a spatially explicit manner. Good OLS or RF model predictions from inventory data at short distance largely result from the strong spatial autocorrelation displayed by both the alpha diversity and the predictive variables rather than necessarily from causal relationships. Our results suggest that alpha diversity is driven by history rather than by the contemporary environment. Given the low predictive power of models, we call for a major effort to broaden the geographical extent and intensity of forest assessments to expand our knowledge of African rain forest diversity.

In this prospective, nationwide study conducted in the Netherlands, the classic triad of fever, stiff neck, and a change in mental status was present in less than half of 696 episodes of bacterial meningitis. The overall mortality rate was 21 percent, but more than 10 percent of survivors had disabilities such as deafness or hemiparesis. New vaccines have changed the pattern of bacterial meningitis in adults, but the rates of death and complications remain high. The epidemiology of bacterial meningitis has changed. Meningitis due to Haemophilus influenzae type b has been nearly eliminated in the Western world since vaccination against H. influenzae type b was initiated, 1 and the introduction of conjugate vaccines against Streptococcus pneumoniae is expected to reduce the burden of childhood pneumococcal meningitis significantly. 2 Although vaccination with a pneumococcal conjugate vaccine is producing herd immunity among adults, the age distribution of meningitis has now shifted to older age groups. 2 , 3 Several studies of clinical features and prognostic factors in adults with bacterial meningitis have been performed; however, all were retrospective and relatively small . . .

AbstractObjectiveTo review and appraise the validity and usefulness of published and preprint reports of prediction models for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital or dying with the disease.DesignLiving systematic review and critical appraisal by the covid-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.Data sourcesPubMed and Embase through Ovid, up to 17 February 2021, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.Study selectionStudies that developed or validated a multivariable covid-19 related prediction model.Data extractionAt least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).Results126 978 titles were screened, and 412 studies describing 731 new prediction models or validations were included. Of these 731, 125 were diagnostic models (including 75 based on medical imaging) and the remaining 606 were prognostic models for either identifying those at risk of covid-19 in the general population (13 models) or predicting diverse outcomes in those individuals with confirmed covid-19 (593 models). Owing to the widespread availability of diagnostic testing capacity after the summer of 2020, this living review has now focused on the prognostic models. Of these, 29 had low risk of bias, 32 had unclear risk of bias, and 545 had high risk of bias. The most common causes for high risk of bias were inadequate sample sizes (n=408, 67%) and inappropriate or incomplete evaluation of model performance (n=338, 56%). 381 models were newly developed, and 225 were external validations of existing models. The reported C indexes varied between 0.77 and 0.93 in development studies with low risk of bias, and between 0.56 and 0.78 in external validations with low risk of bias. The Qcovid models, the PRIEST score, Carr’s model, the ISARIC4C Deterioration model, and the Xie model showed adequate predictive performance in studies at low risk of bias. Details on all reviewed models are publicly available at https://www.covprecise.org/.ConclusionPrediction models for covid-19 entered the academic literature to support medical decision making at unprecedented speed and in large numbers. Most published prediction model studies were poorly reported and at high risk of bias such that their reported predictive performances are probably optimistic. Models with low risk of bias should be validated before clinical implementation, preferably through collaborative efforts to also allow an investigation of the heterogeneity in their performance across various populations and settings. Methodological guidance, as provided in this paper, should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction modellers should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.Systematic review registrationProtocol https://osf.io/ehc47/, registration https://osf.io/wy245.Readers’ noteThis article is the final version of a living systematic review that has been updated over the past two years to reflect emerging evidence. This version is update 4 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

Although methotrexate and cyclosporine are both effective treatments for psoriasis, their comparative efficacy has not been established. This trial compared the two drugs and found them to be similarly effective. Each was associated with specific but limited side effects. The two drugs are similarly effective in moderate-to-severe psoriasis. Chronic plaque psoriasis is a skin disease characterized by sharply demarcated, erythematous, squamous lesions, with an estimated worldwide prevalence of 0.1 to 3 percent. 1 Various therapies are available for the treatment of psoriasis, including topical ointments, such as calcipotriene, corticosteroids, tar, and anthralin; phototherapy with ultraviolet B radiation (UVB) and methoxsalen (psoralen) with ultraviolet A radiation (PUVA); systemic drugs such as acitretin; and the systemic immunosuppressant drugs methotrexate and cyclosporine. 2 , 3 Methotrexate and cyclosporine are often used in daily clinical practice, but which of the two is more effective has not been established. The current management of severe psoriasis is . . .

Background: In some diagnostic accuracy studies, the test results of a series of patients with an established diagnosis are compared with those of a control group. Such case–control designs are intuitively appealing, but they have also been criticized for leading to inflated estimates of accuracy. Methods: We discuss similarities and differences between diagnostic and etiologic case–control studies, as well as the mechanisms that can lead to variation in estimates of diagnostic accuracy in studies with separate sampling schemes (“gates”) for diseased (cases) and nondiseased individuals (controls). Results: Diagnostic accuracy studies are cross-sectional and descriptive in nature. Etiologic case–control studies aim to quantify the effect of potential causal exposures on disease occurrence, which inherently involves a time window between exposure and disease occurrence. Researchers and readers should be aware of spectrum effects in diagnostic case–control studies as a result of the restricted sampling of cases and/or controls, which can lead to changes in estimates of diagnostic accuracy. These spectrum effects may be advantageous in the early investigation of a new diagnostic test, but for an overall evaluation of the clinical performance of a test, case–control studies should closely mimic cross-sectional diagnostic studies. Conclusions: As the accuracy of a test is likely to vary across subgroups of patients, researchers and clinicians might carefully consider the potential for spectrum effects in all designs and analyses, particularly in diagnostic accuracy studies with differential sampling schemes for diseased (cases) and nondiseased individuals (controls).

Posttraumatic stress disorder (PTSD) develops in 10-20% of injury patients. We developed a novel, self-guided Internet-based intervention (called Trauma TIPS) based on techniques from cognitive behavioral therapy (CBT) to prevent the onset of PTSD symptoms. To determine whether Trauma TIPS is effective in preventing the onset of PTSD symptoms in injury patients. Adult, level 1 trauma center patients were randomly assigned to receive the fully automated Trauma TIPS Internet intervention (n=151) or to receive no early intervention (n=149). Trauma TIPS consisted of psychoeducation, in vivo exposure, and stress management techniques. Both groups were free to use care as usual (nonprotocolized talks with hospital staff). PTSD symptom severity was assessed at 1, 3, 6, and 12 months post injury with a clinical interview (Clinician-Administered PTSD Scale) by blinded trained interviewers and self-report instrument (Impact of Event Scale-Revised). Secondary outcomes were acute anxiety and arousal (assessed online), self-reported depressive and anxiety symptoms (Hospital Anxiety and Depression Scale), and mental health care utilization. Intervention usage was documented. The mean number of intervention logins was 1.7, SD 2.5, median 1, interquartile range (IQR) 1-2. Thirty-four patients in the intervention group did not log in (22.5%), 63 (41.7%) logged in once, and 54 (35.8%) logged in multiple times (mean 3.6, SD 3.5, median 3, IQR 2-4). On clinician-assessed and self-reported PTSD symptoms, both the intervention and control group showed a significant decrease over time (P<.001) without significant differences in trend. PTSD at 12 months was diagnosed in 4.7% of controls and 4.4% of intervention group patients. There were no group differences on anxiety or depressive symptoms over time. Post hoc analyses using latent growth mixture modeling showed a significant decrease in PTSD symptoms in a subgroup of patients with severe initial symptoms (n=20) (P<.001). Our results do not support the efficacy of the Trauma TIPS Internet-based early intervention in the prevention of PTSD symptoms for an unselected population of injury patients. Moreover, uptake was relatively low since one-fifth of individuals did not log in to the intervention. Future research should therefore focus on innovative strategies to increase intervention usage, for example, adding gameplay, embedding it in a blended care context, and targeting high-risk individuals who are more likely to benefit from the intervention. International Standard Randomized Controlled Trial Number (ISRCTN): 57754429; http://www.controlled-trials.com/ISRCTN57754429 (Archived by WebCite at http://webcitation.org/6FeJtJJyD).

We assessed the prevalence and distribution of the different causes of transient loss of consciousness (TLOC) in the emergency department (ED) and chest pain unit (CPU) and estimated the proportion of persons with syncope in the general population who seek medical attention from either their general practitioner or the ED/CPU. A review of the charts of consecutive patients presenting with TLOC at the ED/CPU of our university hospital between 2000 and 2002 was conducted. Patients younger than 12 years or with a known epileptic disorder were excluded. Age and sex of syncopal patients were compared with those in a general practice and general population data sets. During the study period, 0.94% of the patients visiting the ED/CPU presented with TLOC (n = 672), of which half had syncope. Only a small but probably selected group of all people with syncope visit the ED/CPU.